Positron Emission Tomography (PET) is a vital imaging modality widely used in clinical diagnosis and preclinical research but faces limitations in image resolution and signal-to-noise ratio due to inherent physical degradation factors. Current deep learning-based denoising methods face challenges in adapting to the variability of clinical settings, influenced by factors such as scanner types, tracer choices, dose levels, and acquisition times. In this work, we proposed a novel 3D ControlNet-based denoising method for whole-body PET imaging. We first pre-trained a 3D Denoising Diffusion Probabilistic Model (DDPM) using a large dataset of high-quality normal-dose PET images. Following this, we fine-tuned the model on a smaller set of paired low- and normal-dose PET images, integrating low-dose inputs through a 3D ControlNet architecture, thereby making the model adaptable to denoising tasks in diverse clinical settings. Experimental results based on clinical PET datasets show that the proposed framework outperformed other state-of-the-art PET image denoising methods both in visual quality and quantitative metrics. This plug-and-play approach allows large diffusion models to be fine-tuned and adapted to PET images from diverse acquisition protocols.

翻译：正电子发射断层扫描（PET）是一种广泛应用于临床诊断和临床前研究的重要成像模态，但由于固有的物理退化因素，其图像分辨率和信噪比面临限制。当前基于深度学习的去噪方法在适应临床环境的多变性方面面临挑战，这些变异性受扫描仪类型、示踪剂选择、剂量水平和采集时间等因素影响。在本研究中，我们提出了一种新颖的基于3D ControlNet的全身PET图像去噪方法。我们首先使用大规模高质量常规剂量PET图像数据集预训练了一个3D去噪扩散概率模型。随后，我们在较小规模的配对低剂量与常规剂量PET图像集上对模型进行微调，通过3D ControlNet架构整合低剂量输入，从而使模型能够适应不同临床环境下的去噪任务。基于临床PET数据集的实验结果表明，所提出的框架在视觉质量和定量指标上均优于其他最先进的PET图像去噪方法。这种即插即用的方法使得大型扩散模型能够通过微调适应来自不同采集协议的PET图像。